{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Recompiling stale cache file /home/ctroupin/.julia/compiled/v1.2/DivandAltimetry.ji for DivandAltimetry [top-level]\n",
      "└ @ Base loading.jl:1240\n"
     ]
    }
   ],
   "source": [
    "push!(LOAD_PATH, \"../\")\n",
    "using Revise\n",
    "using DivandAltimetry\n",
    "using Statistics\n",
    "using PyPlot\n",
    "using Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"./nrt_blacksea_allsat_phy_l4_20190601_20190607.nc\""
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datadir = \"./\"\n",
    "datafile = joinpath(datadir, \"nrt_blacksea_al_phy_vfec_l3_20170422_20170513.nc\")\n",
    "filelist = [\"nrt_blacksea_al_phy_vfec_l3_20190101_20190122.nc\",\n",
    "            \"nrt_blacksea_al_phy_vfec_l3_20190102_20190123.nc\",\n",
    "            \"nrt_blacksea_al_phy_vfec_l3_20190103_20190124.nc\"];\n",
    "gridfile = joinpath(datadir, \"nrt_blacksea_allsat_phy_l4_20190601_20190607.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[32m\u001b[1mTest Passed\u001b[22m\u001b[39m"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@test DivandAltimetry.shiftlon(187.2) == -172.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[32m\u001b[1mTest Passed\u001b[22m\u001b[39m"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obsval,obslon,obslat,obstime = DivandAltimetry.loadaviso_alongtrack(datafile);\n",
    "@test length(obsval) == 71\n",
    "@test obsval[1] ≈ -0.011"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[32m\u001b[1mTest Passed\u001b[22m\u001b[39m\n",
       "      Thrown: MethodError"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@test_throws MethodError DivandAltimetry.loadaviso_alongtrack(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[32m\u001b[1mTest Passed\u001b[22m\u001b[39m"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load from a list of files\n",
    "obsval,obslon,obslat,obstime = DivandAltimetry.loadaviso_alongtrack(filelist[2]);\n",
    "@test obsval[end] ≈ 0.072\n",
    "@test length(obsval) == 68"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Warning: Empty file list\n",
      "└ @ DivandAltimetry /home/ctroupin/Projects/Altimetry-Interpolation/julia/DivandAltimetry.jl:88\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\u001b[32m\u001b[1mTest Passed\u001b[22m\u001b[39m"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obsval,obslon,obslat,obstime = DivandAltimetry.loadaviso_alongtrack([]);\n",
    "@test obslat === nothing\n",
    "@test obsval === nothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the grid\n",
    "gridval,griderr,gridlon,gridlat,gridtime = DivandAltimetry.loadaviso_gridded(gridfile);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[32m\u001b[1mTest Passed\u001b[22m\u001b[39m"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@test isnan(mean(griderr))\n",
    "@test length(gridval) == 6720\n",
    "@test griderr[2] === NaN\n",
    "@test mean(filter(!isnan, gridval)) ≈ 0.1101407629255\n",
    "@test size(gridval) == (120, 56)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[91m\u001b[1mTest Failed\u001b[22m\u001b[39m at \u001b[39m\u001b[1mIn[10]:2\u001b[22m\n",
      "  Expression: r ≈ -0.017482204\n",
      "   Evaluated: 1.2134064202012318 ≈ -0.017482204\n"
     ]
    },
    {
     "ename": "Test.FallbackTestSetException",
     "evalue": "There was an error during testing",
     "output_type": "error",
     "traceback": [
      "There was an error during testing",
      "",
      "Stacktrace:",
      " [1] record(::Test.FallbackTestSet, ::Test.Fail) at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.2/Test/src/Test.jl:737",
      " [2] do_test(::Test.Returned, ::Expr) at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.2/Test/src/Test.jl:526",
      " [3] top-level scope at In[10]:2"
     ]
    }
   ],
   "source": [
    "r = DivandAltimetry.get_aspect_ratio(gridlon);\n",
    "@test r ≈ -0.017482204"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Figure(PyObject <Figure size 1920x1440 with 2 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratio = DivandAltimetry.get_aspect_ratio(gridlat);\n",
    "gradX, gradY = DivandAltimetry.gradient2D(gridval);\n",
    "subplot(2,1,1)\n",
    "pcolormesh(gridlon, gridlat, gradX')\n",
    "gca().set_aspect(ratio)\n",
    "subplot(2,1,2)\n",
    "pcolormesh(gridlon, gridlat, gradY')\n",
    "gca().set_aspect(ratio)"
   ]
  }
 ],
 "metadata": {
  "@webio": {
   "lastCommId": null,
   "lastKernelId": null
  },
  "kernelspec": {
   "display_name": "Julia 1.2.0",
   "language": "julia",
   "name": "julia-1.2"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.2.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
